code: def forward(self, data):
      x, edge_index, batch = (
          data.node_feature,
          data.edge_index,
          data.batch,
      )
      x = self.pre_mp(x)
      for i in range(len(self.convs)):
          x = self.convs[i](x, edge_index)
          x = F.relu(x)
      x = pyg_nn.global_add_pool(x, batch)
      x = self.post_mp(x)
      x = F.log_softmax(x, dim=1)
      return x
  
imports: import torch
  import argparse
  import skip_models
  import torch.nn
  import torch.nn.functional
  import torch_geometric.transforms
  import torch_geometric.nn
  import torch.optim
  from torch.utils.data import DataLoader
  from torch_geometric.datasets import TUDataset
  from sklearn.metrics import *
  from torch.nn import Sequential, Linear, ReLU
  from deepsnap.dataset import GraphDataset
  from deepsnap.batch import Batch
label_desc: Use image node_id 2 for calling the GIN obj's underlying member method code with example usage: obj.forward(data) and returns: x